چکیده انگلیسی

We investigate the impact of trading halts of NYSE-listed stocks on informationally related securities that continue to trade during the period of the halt. Informational relationships are established for companies in the same four-digit SIC industry based on the correlation of returns, volume, volatility, and the adverse selection components of spreads. We find a significant liquidity impact on informationally related securities with spreads and price impact of trades having substantial increases. However, we also find that quoted depths, the number of trades, and trade volume significantly increase. Our results are consistent with the trading halt model of Spiegel and Subrahmanyam [2000. Asymmetric information and news disclosure rules. Journal of Financial Intermediation 9, 363–403] and with the informed trading model of Tookes [2008. Information, trading, and product market interactions: cross-sectional implications of informed trading. Journal of Finance 63, 379–413]. In addition, our results indicate that there is a common liquidity response of informationally related securities to firm-specific trading halts.

مقدمه انگلیسی

Under Rule 123D of the NYSE, the specialist, with the approval of a NYSE floor official, can suspend trading or delay the opening of a stock, to give investors time to assess new firm-specific information. These trading halts result from a variety of causes including order imbalances, news dissemination, or pending news relating to a specific company.3 However, recent theoretical and empirical studies indicate that firm-specific trading halts have a market impact beyond that of the halted stock. Spiegel and Subrahmanyam (2000) (hereafter, SS) present a model based on information asymmetry that shows trading halts are a necessary component of markets. In their model, trading halts act as a signal that substantial information asymmetry exists for stocks that are related to the halted stock, such as stocks in the same industry.
Tookes (2008) develops a model based on Easley et al. (1998) in which industry effects on market microstructure measures are specifically considered. Restricting her analysis to a Cournot duopoly, she demonstrates that firm-specific news at the product market level impacts other related firms’ trading. To illustrate how this might work for trading halts, assume that a company intends to announce FDA approval for a new drug that competes with drugs of several other firms. The company informs the exchange of its pending news announcement and trading is halted in the stock. As Tookes points out, insider trading laws limit any informed trading by principles of the announcing company in its own stock, but these laws have limited restrictions to trading in the stock of competitors. A trading halt is identical to the application of insider trading laws with perfect monitoring. Insiders are unable to trade in their own company's stock due to the halt and must seek other venues to obtain rents from their insider information. Under the SS model, the trading halt acts as an information asymmetry signal, implying a broad reduction in liquidity for informationally related securities. However, under the Tookes model, an informational event in one stock in an industry can instigate informed and insider trading in related stocks in that industry, implying an increase in trading. In this study, we investigate the information content of firm-specific trading halts on informationally related securities in the same industry.
To our knowledge, our paper is the first to focus on informationally related securities that continue to trade during the halt period. However, there is a related body of research dealing with trading halts. Madhavan (1992) shows that firm-specific trading halts are a natural response to excessive asymmetric information. Utilizing a Walrasian framework, Bhattacharya and Spiegel (1991) also demonstrate that trading halts arise when the degree of information asymmetry outweighs other motivations of trading. Edelen and Gervals (2003) propose a model that uses firm-specific trading halts as a method to limit specialist power. Empirically, Lee et al. (1994b) investigate the impact of trading halts on the trading characteristics of the halted stock after the halt is lifted. Christie et al. (2002) examine trading halts on the NASDAQ market and find that volatility, spreads, and volume increase after a halt. Corwin and Lipson (2000) explore the impact of intraday trading halts on the limit order book of the halted stock. More recently, Chakrabarty et al. (2007) evaluate informational content on the limited number of off-NYSE trades of the halted stock, during the period of the NYSE trade suspension. All of these studies focus on the impact of the trading halt on the market characteristics of the halted company.
There is a growing body of literature (e.g., Chordia et al., 2000; Hasbrouck and Seppi, 2001) that documents commonality in order flow, returns and transaction costs for related stocks. The commonality literature coupled with the theoretical work of Spiegel and Subrahmanyam (2000) and Tookes (2008) indicate that a broader look at the impact of trading halts is relevant to the study of market liquidity and informed trading.
Our study adds to the literature in several ways. First, we contribute to the trading halts literature by showing that firm-specific trading halts have significant liquidity impacts on informationally related securities. Although we find that relative and absolute spreads increase significantly, we also find large increases in quoted depth, number of trades, and trading volume. Trades placed during the halt period are shown to have a larger price impact compared to a benchmark period. Our results directly support the implications of the SS model of trading halts and the informed trading model of Tookes (2008).
We show that a firm-specific trading halt is an informational event that impacts the market beyond that of the halted company. This finding also has potential regulatory implications. While current regulation limits insider trading in own-firm trades, there are relatively fewer restrictions on trading in competing firms’ stocks. However, such trading may constitute an unfair informational advantage and may need to be closely monitored and even discouraged to bolster market integrity. Finally, we add to the commonality literature by evaluating the common liquidity impact of firms to firm-specific information. Previous papers such as Hasbrouck and Seppi (2001) assess commonality on a much broader scale. We find evidence of commonality in liquidity response to firm-specific stock risk factors for informationally related securities.
1. Hypotheses development
Spiegel and Subrahmanyam (2000) propose an asymmetric information model showing that without the option to halt trading, the level of asymmetric information in the market may become so large that trading will not occur. Ongoing trading without a halt signals low information asymmetry. A unique feature of the SS model is that the empirical implications are created not for the stock that is halted, but rather for informationally related stocks that continue trading. Proposition 10 in SS states that the ask (bid) pricing function of an informationally related security during the period of the trading halt will be greater (less) than the ask (bid) pricing function during a non-halt period, giving Hypothesis 1a:
Hypothesis 1a. When trading is halted for a stock, stocks in the same industry that are informationally related and continue to trade have reduced liquidity.
Under the SS model, a trading halt signals to the market that informational asymmetries exist for stocks that are informationally related to the halted security. Discretionary liquidity traders, in the sense of Admati and Pfleiderer (1988), observe the signal and withdraw from the market. The market-maker, observing the halt signal and the loss of liquidity, widens spreads and reduces quoted depths. Therefore, the SS model indicates that both the demand for and the supply of liquidity will decrease.
Tookes (2008) establishes a model of informed trading based on stock and industry-specific news events. Under the Tookes model, liquidity traders, discretionary or nondiscretionary, fail to understand and incorporate the halt signal and continue to trade. Based on Lemma 1 of her paper, insiders of the halted company and their proxies enter the market and use their superior knowledge of the industry to place informed trades in informationally related securities. Because insiders are unable to trade their own stock due to the trading halt, a trading halt is equivalent to enforcement of insider trading laws with perfect monitoring. The market-maker and other liquidity suppliers observe the order flow and widen spreads. The wider spread compensates the market-maker for losses to informed traders, at the expense of liquidity traders. The implication is that trade-based measures of liquidity will increase because of the additional volume of informed traders while quote-based measures of liquidity will decrease. The implied market impact under the Tookes model is:
Hypothesis 1b. When trading is halted for a stock, stocks in the same industry that are informationally related and continue to trade have higher trade-based liquidity and lower quote-based liquidity.
In addition to market liquidity measures, we also investigate the price impact of trades in informationally related stocks during the halt. Proposition 8 of the SS model indicates that the derivative of the pricing schedule with respect to quantity will increase for informationally related securities. On the other hand, the sequential trade model of Tookes is limited to a single trading period and does not present any direct evaluation of price impact. However, a larger price impact of trades during the halt is consistent with the increased informed trading of the Tookes model. Our final direct hypothesis is:
Hypothesis 2. When trading is halted for a stock, trades in stocks in the same industry that are informationally related and continue to trade will have larger price impact.4
We further investigate the impact of trading halts on market conditions of informationally related stocks by evaluating the determinants of identified liquidity impacts. Under the SS model, Proposition 7, companies with higher informational relatedness to the halted company should have a higher change in market liquidity during the trading halt. Lemma 2 of the Tookes model indicates that companies with lower market share simply lack the product market impact to affect the market liquidity of other stocks in the same industry. Therefore, the liquidity impact of a trading halt on related securities is increasing in the market share of the halted company and decreasing in the market share of the related security. This indicates that the net liquidity change is determined by the interaction between the market share of the halted company and that of the reference company. Tookes (2008, Eq. (4)) also indicates that smaller companies in the Reference Group (those traded during the halt period) should have the largest change in liquidity, implying that the liquidity impact is decreasing with increasing market capitalization. We test these implications using regression analysis. Beyond the implied liquidity determinants from these models, we also wish to investigate which parameters influence the choice of informed and insider traders’ targets for trading.